An improved grey wolf optimizer for welding shop inverse scheduling

Wang, Cuiyu; Zhao, Li; Li, Xinyu; Li, Yang

doi:10.1016/j.cie.2021.107809

Cited by 15 publications

(12 citation statements)

References 34 publications

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“…Compared with other metaheuristics, GWO has more competitive performance, fast convergence, and a simple search mechanism. It is extensively applied in many areas, including path planning, 15‐19 image segmentation, 20‐22 data mining, 23‐28 engineering optimization, 29‐31 and production scheduling 32‐34 . Among them, Lu et al 32 introduced a variable neighborhood search strategy to improve the performance of the GWO and solved for hybrid flow shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al 33 proposed an improved GWO to solve the distributed flexible flow shop scheduling problem, introduced four crossover operators to extend the search space, and the proposed algorithm was found to have good performance by comparing it with other advanced methods. Wang et al 34 introduced a variable neighborhood structure to enrich the exploitation of the GWO for the scheduling problem in the welding shop. The performance of the proposed algorithm was verified in three instances, and the proposed algorithm was found to be superior in solving the scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

“…It is extensively applied in many areas, including path planning, [15][16][17][18][19] image segmentation, [20][21][22] data mining, [23][24][25][26][27][28] engineering optimization, [29][30][31] and production scheduling. [32][33][34] Among them, Lu et al 32 introduced a variable neighborhood search strategy to improve the performance of the GWO and solved for hybrid flow shop scheduling problem. The results show that the proposed algorithm significantly outperformed its competing algorithms in solving this problem.…”

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confidence: 99%

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Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem

Chen,

Zheng

2023

Concurrency and Computation

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SummaryThe permutation flow shop scheduling problem, as a classical problem in the scheduling field, is an NP‐hard problem. However, most of the reported algorithms are difficult to achieve good accuracy and efficiency. To address this problem, a hybrid grey wolf optimizer (HGWO) is proposed in this paper. First, one cooperative initialization strategy is proposed to improve the quality of the initial solution based on the improved Nawaz‐Enscore‐Ham (NEH) method and the tent chaotic map method. Second, a levy flight strategy is introduced to balance the exploitation and exploration of the algorithm for the problem's characteristics. Third, the crossover and mutation strategy, and the critical block exchange based on critical path strategy are proposed to avoid falling into the local optimum. In addition, for the best individual, the variable neighborhood descent strategy is proposed to enhance the convergence accuracy of the algorithm. To verify the performance of the proposed algorithm, three different types of instances are selected for comparison experiments with other existing methods, and the experimental results show that the proposed HGWO outperforms other comparison algorithms in solving the problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem

Chen,

Zheng

2023

Concurrency and Computation

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“…GWO is an effective metaheuristic, and it attracted the interest of academics when it was initially introduced. It has been widely used in many fields such as feature selection [ 11 , 12 ], image processing [ 13 , 14 ], path planning [ 15 ], weld shop inverse scheduling [ 16 ], and so on. In GWO, the search process is guided by the leading wolves in each iteration, which shows great convergence toward leading wolves.…”

Section: Introductionmentioning

confidence: 99%

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Sun

Feng

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.

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“…And the results show that the GGWO algorithm is able to provide competitive and superior results to the compared algorithms[64].• Improved grey wolf optimizer (IGWO): Wang et al propose an improved grey wolf optimizer (IGWO) to optimize the model. The experimental results show the superiority of the proposed method compared with other algorithms for solving WSISIP[65].• Advanced Grey Wolf Optimization algorithm (AGWO):…”

mentioning

confidence: 96%

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

2022

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This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-MLP model for predicting student achievement is thus obtained. The training and verification of the EGWO-MLP prediction model are conducted based on the thirty attributes from the University of California (UCI) Machine Learning Repository dataset’s student performance dataset, including family features and personal characteristics. For the Mathematics (Mat.) subject achievement prediction, the EGWO-MLP model outperforms one model’s prediction accuracy, and the standard deviation possesses the stable ability to predict student achievement. And for the Portuguese (Por.) subject, the EGWO-MLP outperforms three models’ Mathematics (Mat.) subject achievement prediction through the training process and takes first place through the testing process. The results show that the EGWO-MLP model has made fewer test errors, indicating that EGWO can effectively feedback weights and biases due to the strong exploration and local stagnation avoidance. And the EGWO-MLP model is feasible for predicting student achievement. The study can provide reference for improving school teaching programs and enhancing teachers’ teaching quality and students’ learning effect.

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An improved grey wolf optimizer for welding shop inverse scheduling

Cited by 15 publications

References 34 publications

Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem

Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement

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